AI for Single-cell Research

Our team’s research goal is understanding spatiotemporal heterogeneity across different scales of biological organization, with a focus on how it affects cancer initiation and progression. To achieve this, we combine (spatial) single-cell measurements with artificial intelligence and machine learning approaches to model the complexity of the tumor microenvironment. Understanding heterogeneity across multiple scales has the potential to lead to exciting mechanistic discoveries and pave the way for new precision medicine approaches.

Projects related to this work include:

• AI methods for precision therapies
• Modeling spatial heterogeneity of the tumor microenvironment
• Modeling the 4D genome
• Quantifying biological heterogeneity from single-cell data